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Results of a Hedonic Regression Model That Estimates the Impact of Bus Rapid Transit (BRT) Stations on Surrounding Residential Property Values Along the Pittsburgh East Busway …and All You Ever Wanted to Know About Econometrics….. Victoria Perk Senior Research Associate

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  1. Results of a Hedonic Regression Model That Estimates the Impact of Bus Rapid Transit (BRT) Stations on Surrounding Residential Property Values Along the Pittsburgh East Busway…and All You Ever Wanted to Know About Econometrics….. Victoria Perk Senior Research Associate National Bus Rapid Transit Institute (NBRTI) Center for Urban Transportation Research (CUTR) University of South Florida, Tampa

  2. Background

  3. Impacts of Bus Rapid Transit (BRT) on Surrounding Property Values Victoria Perk Senior Research Associate National Bus Rapid Transit Institute (NBRTI) Center for Urban Transportation Research (CUTR) University of South Florida, Tampa APTA Bus Rapid Transit Conference May 6, 2009

  4. Background • Understanding the relationship between land use and BRT • Issue of permanence • Assess economic development

  5. Previous Literature • No recent quantitative modeling studies on property value impacts of BRT in the U.S. • Previous studies address impacts of rail modes on property values • Isolate effect of distance from transit (either right-of-way, stations, or both) • Typical results find positive impacts on property values from nearby rail transit, but magnitudes are relatively small

  6. Hypothesis • We hoped to find statistically significant, positive impacts on surrounding property values from BRT, with magnitudes approaching those found for rail transit modes.

  7. Methodology • Based on literature reviewed for this effort • Estimate the impacts of BRT on surrounding property values using regression analysis • Hedonic regression model • Estimate the variation in property values due to proximity to BRT stations • Isolate the effect of distance to nearest BRT station from all other (measurable) factors that determine property values • Used SPSS and STATA software

  8. First Application • Pittsburgh East Busway • Operating since 1983 • Serves Downtown Pittsburgh, eastern communities of Pittsburgh, and eastern suburbs of Allegheny County • Average weekday ridership: 25,000 • Annual ridership: 7 million • 9.1 miles in length • 9 stations

  9. Data • Parcel data from Allegheny County Property Assessor’s Office, 2007 • 2000 U.S. Census data, Allegheny County • Crime data from the Pennsylvania Uniform Crime Reporting System • Data set constructed using GIS

  10. Variables • Dependent variable: property value (assessed value) • Key independent variable: distance of parcel to nearest BRT station • Other variables • Property characteristics • Neighborhood characteristics

  11. Property Characteristics • Lot size (sq. ft.) • Living area (sq. ft.) • Number of bedrooms (including interaction term with living area) • Number of full bathrooms, number of half-baths • Condition of property • Age of property (not significant; removed from model) • Distance to nearest BRT station (including squared term to test for nonlinearity) • Distance to nearest LRT station • Distance to nearest freeway entrance (highly collinear with distance to LRT station; removed from model) • Distance to freeway right-of-way

  12. Neighborhood Characteristics • Population density • Median income • Crime per capita (dropped due to data problems) • Municipality fixed-effects (dummy variables)

  13. Preliminary Result • Signs and magnitudes of most coefficients as expected • Every 1,000 feet closer to a station increases market value of single-family home by $836 • Results statistically significant at the 5% level (95% confidence) using heteroskedastic-robust standard errors

  14. Next Steps • Final report available soon (www.nbrti.org) • Update literature review • Refine methodology • Apply to other U.S. cities • Boston • Cleveland • Los Angeles • Eugene…

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